Synthetic Aperture Radar (SAR) images are normally used for ship detection to support fisheries enforcement and control. In particular, for Bluefin tuna (BFT), a large amount of SAR images was used over the Mediterranean Sea, where floating cages are towed by vessels in order to transport tuna alive toward the farms near shore. The aim of this paper is to present the methodology used in order to detect the cages automatically and separate those from the vessels.
Experience and comparison with ground truth data have proved that tuna cages SAR signatures can be distinguished from vessel signatures thanks to their distinctive texture pattern and position with respect to the towing vessel. Different candidate features were extracted from the images: texture features and also some estimates based on K-distribution of pixels¿ intensities. The so called Binary Local Pattern (BLP) texture feature was also used.
The feature selection was performed by leave-one-out (LOO) procedure with the K-Nearest Neighbors (K-NN) classifier and Classification and Regression Tree (CART) methodology. Five features yielded the best results: the estimate of the K-distribution ( ) of pixels¿ intensities; the standard deviation of the amplitude SAR image pixels in the neighborhood of the possible cage or vessel and the three principal component features of the BLP values. The classification performance of the features was finally estimated both with the K-NN and the Multi Layer Perceptron (MLP) classifiers. A K-NN based version of the cage/vessel detection algorithm was used successfully during the last BFT campaign in 2008.